THE ROLE OF COSMIC RAY TRANSPORT IN SHAPING THE SIMULATED CIRCUMGALACTIC MEDIUM Iryna Butsky Blue Waters Graduate Fellow 2016-2017 University of Washington Advisor: Thomas R. Quinn
MOTIVATION (KEY CHALLENGES) METHODS (WHY BLUE WATERS) RESULTS (ACCOMPLISHMENTS)
THE CIRCUMGALACTIC MEDIUM (CGM) Tumlinson, Peeples, Werk 2017
SIMULATIONS REPRODUCE GALACTIC DISK STRUCTURE, BUT NOT CGM Wang et al. 2015
NON-THERMAL SUPERNOVA FEEDBACK: COSMIC RAYS Charged particles (protons) accelerated to relativistic velocities in extreme shocks (supernovae) Propagate along magnetic field lines Provide pressure support to thermal gas Drive outflows Support low-density 10 4 K gas
SIMULATIONS WITH COSMIC RAY FEEDBACK BETTER MATCH OBSERVATIONS no cosmic rays cosmic ray feedback cool gas Column Densities warm gas Radius (kpc) Salem, Bryan, and Corlies 2016 7
SIMULATIONS WITH COSMIC RAY FEEDBACK BETTER sources of uncertainty in modeling MATCH OBSERVATIONS no cosmic rays cosmic ray feedback 1.Fraction of CR energy in cool gas SN Column Densities 2.Transport velocity (diffusion coefficient or warm gas streaming velocity) 3.CR transport approximation: streaming or diffusion? Radius (kpc) Salem, Bryan, and Corlies 2016 8
MOTIVATION (KEY CHALLENGES) RESULTS (ACCOMPLISHMENTS)
MODELING THE COSMIC RAY “FLUID” CRs scattered by variation in magnetic field Diffusion Streaming
Jiang and Oh 2018 DIFFUSION STREAMING
METHODS Suite of isolated disk galaxies (Milky Way type) Supernova source of cosmic rays Differ in cosmic ray transport ENZO astrophysical simulation code (Bryan et al. 2014) Analysis tools: yt (Turk et al. 2011) Trident (Hummels et al. 2016)
WHY BLUE WATERS Huge variation in simulation scale Each cell follows complex interaction rules
WHY BLUE WATERS Efficient parallelization Huge variation in simulation scale Sufficient data storage Each cell follows Awesome support team! complex interaction rules
KEY CHALLENGES (MOTIVATION) WHY BLUE WATERS (METHODS)
CGM TEMPERATURE SENSITIVE TO CR TRANSPORT diffusion streaming
APPLICATION: UNDERSTANDING THE ORIGINS OF O VI diffusion streaming 150 x 150 kpc Butsky and Quinn 2018, submitted to ApJ
DISTRIBUTION OF CR PRESSURE IN THE CGM DEPENDS ON INVOKED TRANSPORT Butsky and Quinn 2018, submitted to ApJ
SUMMARY/FUTURE WORK Cosmic rays are observed to be in equipartition with the turbulent and magnetic pressures in the galaxy Cosmic ray feedback in simulations drives stronger outflows and can reproduce observed ionization structure of the GGM Existing simulations with cosmic ray feedback lack predictive power because simulated cosmic ray transport is poorly constrained Streaming is a better approximation than diffusion, but in reality, both effects are present. Need to model both self-consistently (e.g. Jiang & Oh 2018, Thomas and Pfrommer 2018) Need detailed parameter studies!
THANK YOU!
Butsky and Quinn 2018, submitted to ApJ
FLUID EQUATIONS
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